Bottom Line:
We developed a model that takes into account the known receptor distributions of D1 and D2 receptors, the changes these receptors have on neuron response properties, as well as identified circuitry involved in working memory.Our model suggests that D1 receptor under-stimulation in supragranular layers gates internal noise into the PFC leading to cognitive symptoms as has been proposed in attention disorders, while D2 over-stimulation gates noise into the PFC by over-activation of cortico-striatal projecting neurons in infragranular layers.We apply this model in the context of a memory-guided saccade paradigm and show deficits similar to those observed in schizophrenic patients.

ABSTRACTThe dopaminergic system has been shown to control the amount of noise in the prefrontal cortex (PFC) and likely plays an important role in working memory and the pathophysiology of schizophrenia. We developed a model that takes into account the known receptor distributions of D1 and D2 receptors, the changes these receptors have on neuron response properties, as well as identified circuitry involved in working memory. Our model suggests that D1 receptor under-stimulation in supragranular layers gates internal noise into the PFC leading to cognitive symptoms as has been proposed in attention disorders, while D2 over-stimulation gates noise into the PFC by over-activation of cortico-striatal projecting neurons in infragranular layers. We apply this model in the context of a memory-guided saccade paradigm and show deficits similar to those observed in schizophrenic patients. We also show set-shifting impairments similar to those observed in rodents with D1 and D2 receptor manipulations. We discuss how the introduction of noise through changes in D1 and D2 receptor activation may account for many of the symptoms of schizophrenia depending on where this dysfunction occurs in the PFC.

Figure 1: Network architecture, experiment, and neural responses. (A) The model contained 4 input areas (PC 7a), each with a preferred saccade direction, which projected topographically to layer 3 of four cortical columns (that is, PC neurons coding for 180° projected to layer 3 neurons coding for 180°). The layer 3 neurons also outputted topographically to motor output areas in order to bias motor responses. Layer 5 neurons in each cortical layer received input from the MD/SC in a non-topographic manner. These neurons, in turn, projected to a set of layers involved in updating working memory. This unit was composed of a non-specific inhibitory layer, whose function was to clear working memory after a behavioral response was made, as well as modeled basal ganglia, which disinhibited a thalamic layer and allowed new information to be gated into the cortex via excitatory projections. Red arrows are inhibitory, blue arrows are excitatory, and purple arrows are excitatory + inhibitory. (B) We modeled our experiment after the oculomotor delayed response (ODR) behavioral paradigm. This task is broken down into four stages: fixation, cue, delay, and response. The subject must fixate on a visual screen until a cue is briefly presented. After the cue is flashed there is a delay period (2.5 s in our model) during which the subject must remember where the cue was. Lastly, the subject must saccade to the place on the screen where the subject thought the cue was presented. (C) Typical response of a recorded PFC neuron in the ODR task. In this case, the neuron showed persistent activity when a cue is presented at 180°. This is considered the neurons “preferred direction.” This neuron is non-responsive to cues at other spatial locations (non-preferred directions) (adapted from Wang et al., 2007).

Mentions:
We developed a spiking neural network model that included a dlPFC with four-two layer columns each with a preferred saccade direction, a parietal cortex, basal ganglia, superior colliculus, and four motor output areas (Figure 1A). In addition, the model incorporated dopaminergic neuromodulation, including simulated D1 and D2 receptors. This is similar to a recent model we developed that included D1, α2A, and α1 receptors (Avery et al., 2013). We tested our model on the oculomotor delay response (ODR) task, in which a subject must remember the location of a briefly flashed cue over a delay period then saccade to that location (Figure 1B). Figure 1C shows the firing rate of a PFC neuron that was recorded during an ODR task (Wang et al., 2007). The neuron showed persistent firing during the delay period when the cue was presented at 180°. This is considered the “preferred direction” for this neuron. If the cue was presented at any other spatial location (non-preferred direction), the neuron would not show persistent firing during the delay. Our goal was to build a model that could explain the symptoms of schizophrenia, but that took into account the different distributions and cellular affects that are currently known for D1 and D2 receptors. To develop our model, we used a publicly available simulator, which has been shown to simulate large-scale spiking neural networks efficiently and flexibly (Richert et al., 2011). The total simulation time of the experiment was 5 min. This took approximately 37 min to run on an NVIDIA Tesla M2090 GPU with 6 GB of global memory, 512 cores (each operating at 1.30 GHz) grouped into 16 SMs (32 SPs per SM), and a single precision compute power of 1331.2 GFLOPS.

Figure 1: Network architecture, experiment, and neural responses. (A) The model contained 4 input areas (PC 7a), each with a preferred saccade direction, which projected topographically to layer 3 of four cortical columns (that is, PC neurons coding for 180° projected to layer 3 neurons coding for 180°). The layer 3 neurons also outputted topographically to motor output areas in order to bias motor responses. Layer 5 neurons in each cortical layer received input from the MD/SC in a non-topographic manner. These neurons, in turn, projected to a set of layers involved in updating working memory. This unit was composed of a non-specific inhibitory layer, whose function was to clear working memory after a behavioral response was made, as well as modeled basal ganglia, which disinhibited a thalamic layer and allowed new information to be gated into the cortex via excitatory projections. Red arrows are inhibitory, blue arrows are excitatory, and purple arrows are excitatory + inhibitory. (B) We modeled our experiment after the oculomotor delayed response (ODR) behavioral paradigm. This task is broken down into four stages: fixation, cue, delay, and response. The subject must fixate on a visual screen until a cue is briefly presented. After the cue is flashed there is a delay period (2.5 s in our model) during which the subject must remember where the cue was. Lastly, the subject must saccade to the place on the screen where the subject thought the cue was presented. (C) Typical response of a recorded PFC neuron in the ODR task. In this case, the neuron showed persistent activity when a cue is presented at 180°. This is considered the neurons “preferred direction.” This neuron is non-responsive to cues at other spatial locations (non-preferred directions) (adapted from Wang et al., 2007).

Mentions:
We developed a spiking neural network model that included a dlPFC with four-two layer columns each with a preferred saccade direction, a parietal cortex, basal ganglia, superior colliculus, and four motor output areas (Figure 1A). In addition, the model incorporated dopaminergic neuromodulation, including simulated D1 and D2 receptors. This is similar to a recent model we developed that included D1, α2A, and α1 receptors (Avery et al., 2013). We tested our model on the oculomotor delay response (ODR) task, in which a subject must remember the location of a briefly flashed cue over a delay period then saccade to that location (Figure 1B). Figure 1C shows the firing rate of a PFC neuron that was recorded during an ODR task (Wang et al., 2007). The neuron showed persistent firing during the delay period when the cue was presented at 180°. This is considered the “preferred direction” for this neuron. If the cue was presented at any other spatial location (non-preferred direction), the neuron would not show persistent firing during the delay. Our goal was to build a model that could explain the symptoms of schizophrenia, but that took into account the different distributions and cellular affects that are currently known for D1 and D2 receptors. To develop our model, we used a publicly available simulator, which has been shown to simulate large-scale spiking neural networks efficiently and flexibly (Richert et al., 2011). The total simulation time of the experiment was 5 min. This took approximately 37 min to run on an NVIDIA Tesla M2090 GPU with 6 GB of global memory, 512 cores (each operating at 1.30 GHz) grouped into 16 SMs (32 SPs per SM), and a single precision compute power of 1331.2 GFLOPS.

Bottom Line:
We developed a model that takes into account the known receptor distributions of D1 and D2 receptors, the changes these receptors have on neuron response properties, as well as identified circuitry involved in working memory.Our model suggests that D1 receptor under-stimulation in supragranular layers gates internal noise into the PFC leading to cognitive symptoms as has been proposed in attention disorders, while D2 over-stimulation gates noise into the PFC by over-activation of cortico-striatal projecting neurons in infragranular layers.We apply this model in the context of a memory-guided saccade paradigm and show deficits similar to those observed in schizophrenic patients.

ABSTRACTThe dopaminergic system has been shown to control the amount of noise in the prefrontal cortex (PFC) and likely plays an important role in working memory and the pathophysiology of schizophrenia. We developed a model that takes into account the known receptor distributions of D1 and D2 receptors, the changes these receptors have on neuron response properties, as well as identified circuitry involved in working memory. Our model suggests that D1 receptor under-stimulation in supragranular layers gates internal noise into the PFC leading to cognitive symptoms as has been proposed in attention disorders, while D2 over-stimulation gates noise into the PFC by over-activation of cortico-striatal projecting neurons in infragranular layers. We apply this model in the context of a memory-guided saccade paradigm and show deficits similar to those observed in schizophrenic patients. We also show set-shifting impairments similar to those observed in rodents with D1 and D2 receptor manipulations. We discuss how the introduction of noise through changes in D1 and D2 receptor activation may account for many of the symptoms of schizophrenia depending on where this dysfunction occurs in the PFC.